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NoJunk Moderate Rerank
x402
NoJunk Moderate Rerank is a paid API for AI agents from nojunk-moderate.mick8511.workers.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-28).
Semantically reranks up to 100 documents against a query, returning them ordered best-first for RAG pipelines, search, and deduplication.
Price / call$0.002
Success—
StatusUnknown
How this service works
Text Moderation rerank: Semantic reranking: score up to 100 documents against a query and return them best-first (RAG, search, dedupe).
Output
Returns the input documents reordered from most to least semantically relevant to the query, each with a relevance score. If top_k is specified, only the top-k documents are returned. Documents can include their original id field for downstream reference.
Example use cases
RAG pipeline context selection
“I have 50 retrieved document chunks from my knowledge base and the user asked 'what are the side effects of ibuprofen?' — rerank all 50 chunks against that question and give me the top 10 most relevant ones to put into the LLM context window.”
Enterprise search result reordering
“My search engine returned 30 product docs matching 'cloud storage pricing', but the ranking is poor — can you rerank all 30 by semantic relevance to that query and return the best-first order?”
Deduplication and relevance filtering
“I have 80 candidate news snippets about 'Federal Reserve interest rate decision' and I need to surface the most on-topic ones — rerank them all and give me the top 15.”
Technical details
Endpoint
POSThttps://nojunk-moderate.mick8511.workers.dev/rerank?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need fast, pay-per-call semantic reranking of up to 100 documents without managing your own reranking model infrastructure. It is ideal for RAG pipelines, hybrid search post-processing, and relevance filtering where you want best-first ordering before feeding context to an LLM. Prefer it over embedding-based similarity when you want a dedicated reranking model optimized for query-document relevance rather than general similarity.
Side effects
- More than 100 documents submitted — returns validation error
- Empty documents array — returns 400 bad request
- Query string exceeds 2000 characters — rejected
- Individual document text missing 'text' field when using object format — schema validation error
- Service unavailable or payment failure — 402 or 5xx response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| query | string | The search query |
| top_k | integer | Return only the best top_k documents (default all) |
| documents | array | 1 to 100 documents as strings or {id, text} objects |
Tags
x402
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